Abdesslem Layeb

Work place: Constantine 2 university of Abdelhamid Mehri, NTIC faculty, LISIA laboratory

E-mail: abdesslem.layeb@univ-constantine2.dz

Website: https://orcid.org/0000-0002-6553-8253

Research Interests: Combinatorial Optimization


Abdesslem Layeb is professor in the department of computer science at the University of Constantine. I received my PhD degree in computer science from the University of Constantine, Algeria. I'm interested in the combinatorial optimization methods and their applications to solve several problems from different domains like transportation problems, Bioinformatics and other academic problems.

ORCID ID: https://orcid.org/0000-0002-6553-8253

Author Articles
Novel Feature Selection Algorithms Based on Crowding Distance and Pearson Correlation Coefficient

By Abdesslem Layeb

DOI: https://doi.org/10.5815/ijisa.2023.02.04, Pub. Date: 8 Apr. 2023

Feature Selection is an important phase in classification models. Feature Selection is an effective task used to decrease the dimensionality and eliminate redundant and unrelated features. In this paper, three novel algorithms for feature selection problem are proposed. The first one is a filter method, the second one is a wrapper method, and the last one is a hybrid filter method. Both the proposed algorithms use the crowding distance used in the multiobjective optimization as a new metric to assess the importance of the features. The idea behind the use of the crowding distance is that the less crowded features have great impacts on the target attribute (class), and the crowded features have generally the same impact on the class attribute. To enhance the crowded distance, a combination with other metrics will give good results. In this work, the hybrid method combines between the crowding distance and Pearson correlation coefficient to well order the importance of features. Experiments on well-known benchmark datasets including large microarray datasets have shown the effectiveness and the robustness of the proposed algorithms.

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